TAMUSA-Chat: A Domain-Adapted Large Language Model Conversational System for Research and Responsible Deployment

Researchers present TAMUSA-Chat, a framework for building domain-adapted large language model conversational systems for academic institutions. The system combines supervised fine-tuning and retrieval-augmented generation with transparent deployment strategies and publicly available code.

• →TAMUSA-Chat provides a complete framework for adapting general-purpose LLMs to institutional contexts through supervised fine-tuning.
• →The system integrates modular components enabling reproducible experimentation with training configurations and evaluation protocols.
• →Researchers demonstrate how academic institutions can develop contextually grounded AI agents while maintaining governance compliance.
• →Empirical analysis reveals insights into domain adaptation efficiency and computational resource requirements across model sizes.
• →The publicly available codebase supports continued research into institutional LLM deployment and ethical AI considerations.